Gradient-optimized physics-informed neural networks (GOPINNs): a deep learning method for solving th...
Gradient-optimized physics-informed neural networks (GOPINNs): a deep learning method for solving the complex modified KdV equation
About this item
Full title
Author / Creator
Li, Jiaheng , Chen, Junchao and Li, Biao
Publisher
Dordrecht: Springer Netherlands
Journal title
Language
English
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Publication information
Publisher
Dordrecht: Springer Netherlands
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More information
Scope and Contents
Contents
Recently, the physics-informed neural networks (PINNs) have received more and more attention because of their ability to solve nonlinear partial differential equations via only a small amount of data to quickly obtain data-driven solutions with high accuracy. However, despite their remarkable promise in the early stage, their unbalanced back-propag...
Alternative Titles
Full title
Gradient-optimized physics-informed neural networks (GOPINNs): a deep learning method for solving the complex modified KdV equation
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Author / Creator
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Record Identifier
TN_cdi_proquest_journals_2616480487
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_journals_2616480487
Other Identifiers
ISSN
0924-090X
E-ISSN
1573-269X
DOI
10.1007/s11071-021-06996-x
